tf.linalg.adjoint

Computes the Cholesky decomposition of one or more square matrices.

View aliases

Compat aliases for migration

tf.compat.v1.cholesky, tf.compat.v1.linalg.cholesky

tf.linalg.cholesky(
    input, name=None
)

Used in the notebooks

Used in the tutorials
TensorFlow Probability Case Study: Covariance Estimation Bayesian Gaussian Mixture Model and Hamiltonian MCMC TFP Release Notes notebook (0.12.1)

The input is a tensor of shape [..., M, M] whose inner-most 2 dimensions form square matrices.

The input has to be symmetric and positive definite. Only the lower-triangular part of the input will be used for this operation. The upper-triangular part will not be read.

The output is a tensor of the same shape as the input containing the Cholesky decompositions for all input submatrices [..., :, :].

Args
`input`	A `Tensor`. Must be one of the following types: `float64`, `float32`, `half`, `complex64`, `complex128`. Shape is `[..., M, M]`.
`name`	A name for the operation (optional).

Returns
A `Tensor`. Has the same type as `input`.

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tf.linalg / adjoint

View aliases

Used in the notebooks

Args

Returns